To improve the effectiveness of limited spectral resources, an adaptive orthogonal frequency division multiplexing integrated radar and communications waveform design method is proposed. First, the conditional mutual information (MI) between the random target impulse response and the received signal, and the data information rate (DIR) of frequency selective fading channel are formulated. Then, with the constraint on the total power, the optimization problem, which simultaneously considers the conditional MI for radar and DIR for communications, is devised, and the analytic solution is derived. With low transmit power, the designed integrated waveform outperforms the fixed waveform (i.e., equal power allocation). Finally, several simulated experiments are provided to verify the effectiveness of the designed waveform.

In the intelligent transportation system, it is an efficient way for the intelligent vehicle to use the integrated radar and communications system (IRCS) to obtain the range and velocity estimations of other vehicles and simultaneously communicate with other facilities, such as vehicles and base stations. In the IRCS, the transmitted waveform is the orthogonal frequency division multiplexing (OFDM) integrated radar and communications waveform that contains communications information. Due to the existence of communications information, the traditional range and velocity estimation methods in radar can not be directly utilized in the IRCS. Moreover, to improve the resolution of range and velocity estimations, the signal bandwidth and coherent processing interval (CPI) are usually required to be increased, which will result in the increase of system cost and the reduction of update rate. To solve these problems, a auto-paired super-resolution range and velocity estimation method is proposed by using the OFDM integrated radar and communications waveform. First, the communications information in the received signals is compensated. Then, the frequency smoothing is performed to reduce the correlation between the echoes reflected by different targets. Finally, the auto-paired super-resolution range and velocity estimations are obtained by exploiting the translational invariance structure of the underlying signal model in both frequency and pulse domains. Furthermore, Cramr-Rao bounds (CRBs) on range and velocity estimations with the OFDM integrated radar and communications waveform are derived. Several numerical examples are presented to verify the effectiveness of the proposed method.

In the integrated radar and communication (RadCom) systems, the integrated orthogonal frequency division modulation (OFDM) waveform with interleaved subcarriers (IS) can simultaneously perform radar and communication functions. However, the autocorrelation function (ACF) of the integrated waveform may have high sidelobe level with high probability due to the discontinuous spectrum for radar. To solve this problem, in this letter, a joint subcarrier and power allocation method is proposed to minimize the peak-to-sidelobe ratio (PSLR) while meeting the requirement of the communication data rate (CDR) and maintaining a certain range resolution. The formulated optimization problem is nonconvex, and we propose the cyclic minimization algorithm to obtain the subcarrier and power allocation. Finally, several numerical results are provided to verify the effectiveness of the proposed method.

High computational complexity and phase errors (PEs) are the main limitations of time-domain (TD) synthetic aperture radar (SAR) imaging algorithms. Accelerated fast backprojection (BP) (AFBP) algorithm avoids interpolation through wavenumber spectrum connection and is an efficient fast TD imaging algorithm. In order to deal with the image defocusing problem caused by PEs effectively and ensure rapid imaging, a TD autofocus method is proposed in this article, which is based on generalized sharpness metrics and the AFBP imaging model. The autofocus method is divided into two stages. First, for each subaperture (SA), the PE estimation model is established in unified polar coordinate (UPC), where the strong-scattering range-cell pixels are chosen to reduce memory burden and avoid repetitive imaging. The PE estimation is converted into a nonconvex optimization problem. Then, the genetic algorithm (GA) and the maximizing-maximum-pixel-value (MMPV) method are used to estimate the PEs. Second, SA images’ matching and constant PE’s compensation are performed to eliminate the residual PEs. The full-aperture well-focused image is obtained by the coherent accumulation of SA images. The effectiveness of the proposed method is proven by the results of simulation and real SAR data processing.